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Meta interview notes, page 92 of 238

FAANG / Big Tech

Most of these Meta write-ups come from Software Engineers (1,194), then Data Scientists (489) and Product Managers (453). The most reported rounds are the technical phone screen (1,190), the product sense round (466), the system design round (255) and the behavioral / leadership round (205).

2377 interview notes · updated Jul 2026

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with one analytical question about diagnosing a metric spike. Short and focused, no behavioral stuff from what I could tell.
  • You notice a sudden spike in daily active users in Indonesia on Facebook. How do you investigate what caused it?

“My first instinct was to jump straight to 'check if it's a data issue' and I think that was the right call, but I fumbled the structure a bit after that.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Meta SWE coding round, all one problem split into three progressive levels. The whole thing was a custom in-memory key-value store with fields, and each level piled on more complexity. Solid problem if you like building things incrementally, though level 3 had me second-guessing my TTL logic the whole time.
  • Implement a basic in-memory record store supporting set, get, and delete operations on string key-field-value triples.
  • Extend the store with scan and prefix-based scan operations that return fields sorted lexicographically.
  • Add timestamp and TTL support to all operations: set_at, set_at_with_ttl, delete_at, get_at, scan_at, and scan_by_prefix_at.

“Straightforward to start.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Meta with a product sense question framed around Trader Joe's. Single question, case-style, required competitive analysis and feature design thinking.
  • You're a PM at Trader Joe's and sales revenue is declining. How would you design a new user experience drawing inspiration from competitors like Blue Apron?

“This one tripped me up a bit because it's a weird mashup of a metrics question and a design question.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Stats-heavy technical screen for a Data Scientist role at Meta. The whole thing was built around probability and hypothesis testing in a social media analytics context, which felt pretty focused but also a bit relentless.
  • If the probability that a single user comment is positive is p, what is the probability that two independent comments are both positive?
  • Assuming each comment response is independent and identically distributed, if a user's first three responses are positive, what is the probability their fourth response is also positive?
  • Model A returns a positive response 80% of the time and Model B returns one 90% of the time. At the 5% significance level, can you conclude Model B is better? Walk through the statistical test, your null and alternative hypotheses, the test statistic, and the decision rule.

“Straightforward multiplication rule stuff.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist interview with a machine learning system design question around building a personalized recommendation engine. Pretty technical, they wanted you to go beyond just naming algorithms and actually think through constraints and serving.
  • What ML techniques would you use to build ranked recommendation lists for users, and how would you handle user roles, context, or other constraints when deciding what to surface?

“I started with collaborative filtering and then pivoted to learning-to-rank pretty quickly, but I fumbled when they pushed on contextual constraints.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Phone screen for a senior SWE role at Meta, second problem in the loop was a classic linked-list question. Nothing shocking but the bar for explanation clarity felt higher than I expected.
  • Given the head of a singly-linked list and an integer n, remove the nth node from the end of the list in a single pass and return the modified head.

“I knew the two-pointer trick going in, but I fumbled explaining the invariant out loud.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product sense round at Meta for a PM role, one question about whether you'd expand Uber into food delivery if you were the PM at the time. Pretty open-ended, more about how you think than any single right answer.
  • You're the PM for Uber when it's still just a ride-hailing app. Do you expand into food delivery (what would become Uber Eats)?

“I went straight into market sizing and kind of lost the thread for a minute.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Meta for a software engineer role. The prompt was a ride-sharing platform and it covered a decent amount of ground: location updates, driver matching, trip state management, and resilience under load. Felt like a solid mid-to-senior level design question.
  • Design a ride-sharing system that handles rider trip requests, frequent driver location updates, nearby driver matching, ETA and trip state tracking, peak load, and regional outages.

“This is a meaty one.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Probability and stats question for a DS role at Meta, focused on ad insertion strategies. Pretty clean interview, nothing too wild, but the math needs to be tight or you'll fumble the comparison part.
  • Under Strategy A, each post independently has a 4% chance of being an ad. What is the expected number of ads shown in 100 posts?
  • Strategy B guarantees exactly one ad per block of 25 posts. How does the expected number of ads per 100 posts compare between Strategy A and Strategy B?
  • Still under Strategy A, what is the probability that a user sees at least one ad in the next 25 posts?

“Straightforward linearity of expectation.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for an MLE role, basically one big question about your best project. Not much to report in terms of breadth but the depth they expected was real.
  • Walk me through the project you're most proud of. What was the problem, what did you build, what decisions did you make technically and from a business standpoint, and what was the measurable impact?

“I picked a project I knew cold but underestimated how much they'd push on the business side.”

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